Calendar

During the fall and spring semester the Computational Social Science (CSS) and the Computational Sciences and Informatics (CSI) Programs holds weekly seminars where students, faculty and guest speakers present their latest research. These seminars are free and are open to the public.

For CSI, the seminars take place in Exploratory Hall, Room 3301 on Mondays from 4:30 p.m. to 5.40 pm.

For CSS, the seminars take place in Center for Social Complexity Suite which is located Research Hall, Level 3. The seminars start at 3:00 p.m. and normally last until 4:30 p.m. For a list of past CSS seminars click here.

In addition we also host ad hoc seminars relating to guest speakers and students dissertation proposals/defenses which don’t fall under our normal seminars.

If you would like to join the seminar mailing list please email Karen Underwood.

Adverse selection is the phenomenon of “rigged trades” created by asymmetric information between buyers and sellers. This becomes significant in the case of health insurance, where a buyer may know more about his or her health than the insurer. The concern is that widespread adverse selection may lead to a “death spiral”, where premiums become too costly for healthy people to afford, and the only subscribers left are unhealthy people requiring costly health care. Both before and after the passing of the Patient Protection and Affordable Care Act (PPACA, also known as “Obamacare”) in 2013, the concern for adverse selection has been assessed by a number of different methods of economic modeling, most notably game theory models, econometrics, and microsimulation. Kevin Comer utilizes agent-based modeling to assess the emergence and effects of adverse selection on a simulated individual health insurance market, and to test the parameters of already existing policy (coverage and individual mandates, risk corridors, medical loss ratios, and health insurance exchanges) to assess their long-term feasibility.

About the presenter: Kevin Comer is a Senior Simulation Modeling Engineer at MITRE Corporation. He received his B.S in Systems Engineering and Economics from the University of Virginia, and his M.S. in Operations Research from George Mason University. He is currently a Computational and Social Science Ph.D. Candidate in the Department of Computational and Data Sciences at George Mason University. Kevin is scheduled to defend his dissertation in Spring 2017.

The emergence of agriculture played an important role in human history as it allowed people to move from a nomadic (i.e. hunter-gather) to a sedentary (i.e. agricultural) lifestyle. This shift in lifestyle not only provided abundant food, but also sufficient numbers of non-cultivating specialists, which are necessary conditions for the rise of a civilization. However, questions about how and why agriculture originated have remained controversial. This paper explores the origin hypotheses of agriculture, using the canonical theory of social complexity as a framework to study the transition from hunter-gatherer to agricultural societies in the region of the Peiligang in China based on existing literature, and develops an agent-based model to simulate the transition process. The model assumes that a combination of population growth and gaining knowledge on plants drove the transition from hunter-gatherer to agriculture. Results show that based on the basic hypotheses and assumptions, the model is able to generate the key phases that are identical with the existing literature on such a transaction.

COMPUTATIONAL SOCIAL SCIENCE – Towards an ABM for Civil Revolution: Modeling Emergence of Protesters, Military Decisions, and Resulting State of the Institution – Salwa Ismail
@ Center for Social Complexity Suite 3rd Floor, Research Hall, Fairfax Campus

The recent string of events in the Middle East, dubbed as Arab Spring transcended rapidly. There was no mechanism to predict them or their outcome. While there are a few models that forecast rebellion, most of them do not take into account the ability of different factors, such as emotional threshold, of both the citizens and military and their the ability to be influenced by vision of what is going around the agent geographically, along with the influence of media/communication channels, to form a realm of influence and affect the actions of the agents simultaneously. This paper explores an agent-based model whose agents react based on economic and emotional levels and a rebellion ensues. Once the rebellion has begun, there are several other factors in this agent-based model that decide the outcome of the rebellion including agents being killed, their geographic vision, their inclement towards news/media, being influenced by current events, and also their personality type of A or B; all these factors combined together affect the dynamics of the unanticipated revolution. The results of the model are rendered in a short duration of time, as one would expect of revolutions, except for those that plunder into a civil war state. The model could be used as one of many components for forecasting future rebellions that have a combination of factors present, as those discussed this paper.

We investigate the interplay between technological change and macroeconomic dynamics in an agent-based model of the formation of production networks. On the one hand, production networks form the structure that determines economic dynamics in the short run. On the other hand, their evolution reflects the long-term impacts of competition and innovation on the economy. We account for process innovation via increasing variety in the input mix and hence increasing connectivity in the net- work. In turn, product innovation induces a direct growth of the firm’s productivity and the potential destruction of links. The interplay between both processes generate complex technological dynamics in which phases of process and product innovation successively dominate. The model reproduces a wealth of stylized facts about industrial dynamics and technological progress, in particular the persistence of heterogeneity among firms and Wright’s law for the growth of productivity within a technological paradigm. We illustrate the potential of the model for the analysis of industrial policy via a preliminary set of policy experiments in which we investigate the impact on innovators’ success of feed-in tariffs and of priority market access.

Agent-based modeling was lacking in the Python community until Mesa. Mesa is an open-source, liberally licensed, agent-based modeling framework built in Python 3. It allows users to quickly build models by providing a series of reusable components off of which to build. This includes things like agents, space, and time. Mesa is also flexible and decoupled for greater efficiency. It has a back-end that handles the model processing and a browser-based front-end that handles the visualization. It also allows for custom visualizations to be added, as well. Lastly, users can use the Python ecosystem for analyzing data with ease with tools like Jupyter notebooks and Pandas. The goal of Mesa is to provide a Python alternative to other agent-based modeling frameworks such as Netlogo and Mason.

The livelihoods of Chinese rural households have been undergoing a transformation amid rapid urbanization. Though participation in the urban economy has led to improved rural living standards, rural income has consistently lagged behind urban income, and a broader prosperity gap persists between urban and rural areas. Meanwhile, increasing non farm income is associated with the decline of agriculture, especially in those regions with relatively high industrial development. In this seminar I present an agent-based model developed to explore future rural development. The model represents land-use and livelihood decisions of farmer households in the Poyang Lake area, informed by household surveys and interviews. The model experiments aid our understanding about (1) the potential effects of three subsidy policies in villages with different farmland endowments and at different stages of urbanization, and (2) the resilience of rural development under plausible environmental and economic shocks. I discuss how policy may need to differentiate across locations and adapt in the near future to effectively promote rural development amid social and environmental changes.

COMPUTATIONAL SOCIAL SCIENCE – Complex Intelligence Preparation of the Battlefield: An Effort to Operationalize the Integration of Political Theory to Improve Analysis Across the Intelligence Enterprise – Thomas Pike
@ Center for Social Complexity Suite 3rd Floor, Research Hall, Fairfax Campus

Fifteen years of conflict have shown severe limitations in the United States’ ability to influence foreign populations in pursuit of national objectives. Intertwined within these challenges is the difficulty the U.S. Intelligence Enterprise has in integrating recent research to more effectively analyze foreign populations and support decision makers. The introduction of a Complex Adaptive Systems based meta-framework for intelligence analysts, supported by an agent based model can reduce the cost of analysts learning and applying new research. As a first attempt we adopt the Army’s current framework Intelligence Preparation of the Battlefield (IPB) and begin to formulate one possible meta-perspective Complex Intelligence Preparation of the Battlefield. Complex IPB has the potential to be a constantly improving model that integrates new and emerging theories from economics, communication, politics, demography, game theory and social network analysis to analyze the emergence and contagion of civil conflict in local populations. Complex IPB can assist in identifying regions of potential instability before escalation. Finally, a quasi-global sensitivity analysis identifies effective and efficient policy levers in the face of limited resources.

The CSS seminar format for Friday, April 21 will be an “Open Mic” session for CSS PhD students to present their research ideas to their peers prior to starting their projects. Peer feedback in encouraged at this event

Some points for presenters to consider are:

Do you have a paper that is ‘stalled’ and in need of some help to push it to the finish line?

Is one of your models producing interesting results but also doing wacky things?

Do you have exciting results but can’t figure out how to visualize/display them?

Do you need advice on how to calibrate/estimate your theoretical model with data?

The ‘Hidden Trump Model’: Modeling social desirability bias through ABMs

Social desirability bias is a tendency people have to lie about their opinions if they perceive they will be judged or rejected. We present an Opinion Dynamics model in which agents may not be truthful about their opinions when they interact with their social circle. We model two processes through which agents influence one another: an online anonymous process in which agents can interact with anyone and do not fear social rejection, and a face-to-face process where they interact only with friends and may feel compelled to conform. In a political setting, this would apply to a race in which one of the candidates bears a social stigma and therefore some agents are reluctant to voice support for him or her. The results that these nonlinear and asymmetrical processes will have on the overall electorate are not obvious, and are well-suited to an agent-based study.

We hypothesize that this model will produce a “poll bias” of the kind we saw in the 2016 Presidential election — i.e., a significant difference between the number of agents who say they will vote for a candidate and the number who actually do so on election day. We present an analysis of this “Hidden Trump model” and describe the way in which poll bias depends on the strength of the various interaction processes.

Abstract: In January of 2017, Forbes Magazine listed the top technical job skills showing the highest growth in demand from 2011 to 2015. The number three position, with 1,581% growth, was Tableau, a software solution that helps people see and understand their data.

Tableau offers free licenses for academic research.

In this session, Paul Albert will:

Provide a hands-on overview of Tableau to show how it can help people do more with their data

Show examples of Tableau data visualizations relevant to the CSS world

Discuss how Tableau might be able to alter paradigms for sharing academic findings

Discuss free resources available to learn more about Tableau

Paul recently retired from Tableau and has started graduate studies with the GMU Art History program. His initial focus is on applying quantitative analysis and social theory to art markets. His secondary focus is on exploring how products like Tableau can be used to support new ways of presenting academic research and findings.

While at Tableau, Paul coordinated and conducted over 60 training events for over 1,100 participants. Paul was also one of four finalists, out of a field of over 200 contestants, in the annual Tableau “Viz Wiz” data visualization contest for 2016.